The field of the disclosure relates generally to data management, and more specifically, to systems and methods for creating intuitive context for analysis data.
Product design, analysis and validation contribute significant time and cost to the lifecycle of aircraft development as well as an amount of risk. Incorrect assumptions in engineering data and calculations can lead to unanticipated changes, rework and support costs, in varying degrees, in all stages of the development lifecycle as well as the product lifecycle. In addition, at least some engineering data are not considered valuable in-and-of-itself, and are traditionally treated as byproducts, in some cases simply discarded. Engineering data can be volatile, existing temporally for brief periods—as in intermediate calculations that are not correctly documented; or persistent, thereby existing for significant periods of time. Data may also exist globally—shared and persisted across a broad range of stakeholders for the life of the product, or locally, relevant only to the private calculations performed by an engineer.
Essentially, the various forms of engineering data each have a lifecycle of its own, and managing this data provides strategic and competitive advantages that should not be overlooked. Effectively managing the data lifecycle also offers the potential of mitigating the risk posed to overall product lifecycle.
Commercial analysis data management (ADM) systems provide data management, versioning, and persistence, but this data management occurs at the document/file level, which typically limits and controls exposure to the embedded engineering data, sometimes leading to piecemeal validation of analysis data. In one view, ADMs exist only to organize systems of files and automate file-driven processes, and therefore fall short of a capability to manage and exploit engineering data at a granular level (i.e. name-value pairs, attributes and/or properties), and associated metadata, for long periods of time.
Compounding the above, ADM vendors also provide no guarantee of the availability of their services and tools as may be required, for example, over the decades of an expected product lifecycle. ADM tools that consume, interpret, annotate, and persist engineering data do not guarantee a capability to reconstitute data over time (read: decades) as may be required. Further, the data generated through these ADM tools may be in a proprietary format. In such a scenario, valuable data and/or context for the data might be found in the meta-data that is generated by the ADM tools. However, such meta-data is generally not provided as an output of the ADM tool, at least not in a fashion that it might be accessed at a point further down a product lifecycle.
Further, data persisted in vendor file formats only have value when paired with the application that produced it, or by those applications or systems that are sanctioned by the vendor. Therefore, to utilize file-based ADM data in associated application it is often necessary to duplicate, recreate, or extract and transform data using single-purpose transient programmatic efforts, thereby producing orphan data sources. Efforts to manage the data association are intractable since granular access to the ADM data is not allowed.
Interrogating ADM systems for contextualized data (e.g. hierarchical model data) is limited to the capabilities of the ADM system. Each ADM approaches the management of data, and its context—if available—in unique ways and are generally not designed to be accessed or queried outside of the native applications. In a relevant example, aircraft may be used for 50 to 100 years and hence there is a need to have the data needed for future analysis persist for extended periods of time.